Bhaskar Dasgupta, Jie Liang

Models and Algorithms for Biomolecules and Molecular Networks (eBook, ePUB)

• Format: ePub

Produktbeschreibung

By providing expositions to modeling principles, theories, computational solutions, and open problems, this reference presents a full scope on relevant biological phenomena, modeling frameworks, technical challenges, and algorithms. * Up-to-date developments of structures of biomolecules, systems biology, advanced models, and algorithms * Sampling techniques for estimating evolutionary rates and generating molecular structures * Accurate computation of probability landscape of stochastic networks, solving discrete chemical master equations * End-of-chapter exercises

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 274
• Erscheinungstermin: 7. Januar 2016
• Englisch
• ISBN-13: 9781119162278
• Artikelnr.: 44449468

Autorenporträt

BHASKAR DASGUPTA is a Professor in the Computer Science department at the University of Illinois at Chicago, USA. He has written numerous bioinformatics research papers. Dr. DasGupta was the recipient of the NSF CAREER award in 2004 and the UIC College of Engineering Faculty Teaching award in 2012. JIE LIANG is the Richard and Loan Hill Professor within the Department of Bioengineering and Department of Computer Science at the University of Illinois at Chicago, USA. He earned his Ph.D. in Biophysics. He was an NSF CISE postdoctoral research associate (1994-1996) at the Beckman Institute and National Center for Supercomputing and its Applications (NCSA), as well as a visiting fellow at the NSF Institute of Mathematics and Applications at Minneapolis. He was a recipient of the NSF CAREER award in 2003. He was elected a fellow of the American Institute of Medicine and Biological Engineering in 2007. He was a University Scholar (2010-2012).

Inhaltsangabe

List of Figures xiii List of Tables xix Foreword xxi Acknowledgments xxiii 1 Geometric Models of Protein Structure and Function Prediction 1 1.1 Introduction
1 1.2 Theory and Model
2 1.2.1 Idealized Ball Model
2 1.2.2 Surface Models of Proteins
3 1.2.3 Geometric Constructs
4 1.2.4 Topological Structures
6 1.2.5 Metric Measurements
9 1.3 Algorithm and Computation
13 1.4 Applications
15 1.4.1 Protein Packing
15 1.4.2 Predicting Protein Functions from Structures
17 1.5 Discussion and Summary
20 References
22 Exercises
25 2 Scoring Functions for Predicting Structure and Binding of Proteins 29 2.1 Introduction
29 2.2 General Framework of Scoring Function and Potential Function
31 2.2.1 Protein Representation and Descriptors
31 2.2.2 Functional Form
32 2.2.3 Deriving Parameters of Potential Functions
32 2.3 Statistical Method
32 2.3.1 Background
32 2.3.2 Theoretical Model
33 2.3.3 Miyazawa--Jernigan Contact Potential
34 2.3.4 Distance-Dependent Potential Function
41 2.3.5 Geometric Potential Functions
45 2.4 Optimization Method
49 2.4.1 Geometric Nature of Discrimination
50 2.4.2 Optimal Linear Potential Function
52 2.4.3 Optimal Nonlinear Potential Function
53 2.4.4 Deriving Optimal Nonlinear Scoring Function
55 2.4.5 Optimization Techniques
55 2.5 Applications
55 2.5.1 Protein Structure Prediction
56 2.5.2 Protein--Protein Docking Prediction
56 2.5.3 Protein Design
58 2.5.4 Protein Stability and Binding Affinity
59 2.6 Discussion and Summary
60 2.6.1 Knowledge-Based Statistical Potential Functions
60 2.6.2 Relationship of Knowledge-Based Energy Functions and Further Development
64 2.6.3 Optimized Potential Function
65 2.6.4 Data Dependency of Knowledge-Based Potentials
66 References
67 Exercises
75 3 Sampling Techniques: Estimating Evolutionary Rates and Generating Molecular Structures 79 3.1 Introduction
79 3.2 Principles of Monte Carlo Sampling
81 3.2.1 Estimation Through Sampling from Target Distribution
81 3.2.2 Rejection Sampling
82 3.3 Markov Chains and Metropolis Monte Carlo Sampling
83 3.3.1 Properties of Markov Chains
83 3.3.2 Markov Chain Monte Carlo Sampling
85 3.4 Sequential Monte Carlo Sampling
87 3.4.1 Importance Sampling
87 3.4.2 Sequential Importance Sampling
87 3.4.3 Resampling
91 3.5 Applications
92 3.5.1 Markov Chain Monte Carlo for Evolutionary Rate Estimation
92 3.5.2 Sequentail Chain Growth Monte Carlo for Estimating Conformational Entropy of RNA Loops
95 3.6 Discussion and Summary
96 References
97 Exercises
99 4 Stochastic Molecular Networks 103 4.1 Introduction
103 4.2 Reaction System and Discrete Chemical Master Equation
104 4.3 Direct Solution of Chemical Master Equation
106 4.3.1 State Enumeration with Finite Buffer
106 4.3.2 Generalization and Multi-Buffer dCME Method
108 4.3.3 Calculation of Steady-State Probability Landscape
108 4.3.4 Calculation of Dynamically Evolving Probability Landscape
108 4.3.5 Methods for State Space Truncation for Simplification
109 4.4 Quantifying and Controlling Errors from State Space Truncation
111 4.5 Approximating Discrete Chemical Master Equation
114 4.5.1 Continuous Chemical Master Equation
114 4.5.2 Stochastic Differential Equation: Fokker--Planck Approach
114 4.5.3 Stochastic Differential Equation: Langevin Approach
116 4.5.4 Other Approximations
117 4.6 Stochastic Simulation
118 4.6.1 Reaction Probability
118 4.6.2 Reaction Trajectory
118 4.6.3 Probability of Reaction Trajectory
119 4.6.4 Stochastic Simulation Algorithm
119 4.7 Applications
121 4.7.1 Probability Landscape of a Stochastic Toggle Switch
121 4.7.2 Epigenetic Decision Network of Cellular Fate in Phage Lambda
123 4.8 Discussions and Summary
127 References
128 Exercises
131 5 Cellular Interaction Networks 135 5.1 Basic Definitions and Graph-Theoretic Notions
136 5.1.1 Topological Representation
136 5.1.2 Dynamical Representation
138 5.1.3 Topological Representation of Dynamical Models
139 5.2 Boolean Interaction Networks
139 5.3 Signal Transduction Networks
141 5.3.1 Synthesizing Signal Transduction Networks
142 5.3.2 Collecting Data for Network Synthesis
146 5.3.3 Transitive Reduction and Pseudo-node Collapse
147 5.3.4 Redundancy and Degeneracy of Networks
153 5.3.5 Random InteractionNetworks and Statistical Evaluations
157 5.4 Reverse Engineering of Biological Networks
159 5.4.1 Modular Response Analysis Approach
160 5.4.2 Parsimonious Combinatorial Approaches
166 5.4.3 Evaluation of Quality of the Reconstructed Network
171 References
173 Exercises
178 6 Dynamical Systems and Interaction Networks 183 6.1 Some Basic Control-Theoretic Concepts
185 6.2 Discrete-Time Boolean Network Models
186 6.3 Artificial Neural Network Models
188 6.3.1 Computational Powers of ANNs
189 6.3.2 Reverse Engineering of ANNs
190 6.3.3 Applications of ANN Models in Studying Biological Networks
192 6.4 Piecewise Linear Models
192 6.4.1 Dynamics of PL Models
194 6.4.2 Biological Application of PL Models
195 6.5 Monotone Systems
200 6.5.1 Definition of Monotonicity
201 6.5.2 Combinatorial Characterizations and Measure of Monotonicity
203 6.5.3 Algorithmic Issues in Computing the Degree of Monotonicity M
207 References
209 Exercises
214 7 Case Study of Biological Models 217 7.1 Segment Polarity Network Models
217 7.1.1 Boolean Network Model
218 7.1.2 Signal Transduction Network Model
218 7.2 ABA-Induced Stomatal Closure Network
219 7.3 Epidermal Growth Factor Receptor Signaling Network
220 7.4 C. elegans Metabolic Network
223 7.5 Network for T-Cell Survival and Death in Large Granular Lymphocyte Leukemia
223 References
224 Exercises
225 Glossary 227 Index 229